A Railway network is a vast and complex system which is further divided into various small sub-systems. Although some automation is there to control train operations and plan their schedule, however, a large man power is also engaged to manage the planning and operation of railway networks.
Hitherto, at many of the control stations, controllers use train graphs to manually predict train arrival and departure times. But since a long time, it has been a challenge for railway management authorities to overcome constant operational disruptions, big and small. Such disruptions are handled manually. This manual task is very time consuming, error prone and, above all, sub optimal.
In order to address the above summarized problems, many solutions have been proposed. Hitherto, though many systems and solutions are disclosed, they seldom may address issues related to train operations considering ground realities and external condition associated therewith.
The system and solutions disclosed in the prior arts are more often of academic nature and are inclined to take into account only some operating issues. Such models attempt to automate conflict resolution in railway plans. While the solutions and systems disclosed hitherto may provide insights for a functional automation of plurality of tasks, they do not cover the ground realities of arcane railway operating practices, policies and myriad operating details. Therefore, to resolve such a critical transportation problem associated in dealing with optimal & reactive planning of transportation operations, a flexible system that operates to make each node in the operation an intelligent node is required. Such intelligence delivered to each operational node is further required to be optimal, rapidly responsive, realistic, and user friendly.